Classification of rock types using multivariate analysis in the Dezak area, SW Iran
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Classification of rock types using multivariate analysis in the Dezak area, SW Iran Ghazal Hajnajafi1 · Alireza Jafari‑Rad1 · Peyman Afzal2 · Seyed‑Jamal Sheikh‑Zakariaee1 Received: 2 April 2020 / Accepted: 18 August 2020 © Springer Nature Switzerland AG 2020
Abstract The geochemical multivariate techniques were applied to recognize different geological units and categorized rock types which accompanied by different penalties. Knowing changes and classification of raw materials with macroscopic and microscopic quantification have direct effect on economic and efficiency in a precipitated calcium carbonate plant. Based on lithological changes, the Darian Formation was divided into four rock units based on a total of 492 chip samples collected from the study area. The main aim objective of this study was to classify the rock types in this area using a multivariate analysis combining the R-mode factor analysis and K-Means method. Due to the opposite vector orientation a2O, MnO, TiO2, P2O5, SrO, and S O3 versus C aCO3, the lowest geochemical similarity and of SiO2, Al2O3, Fe2O3, K2O, MgO, N maximum separation distance were observed between CaCO3 and other parameters using the R-mode factor analysis, which has also been verified by the Ward’s hierarchical agglomerative clustering method. Complementary Q-mode analysis was applied to determine the position of the samples (eigenvalues) with respect to the variables (eigenvectors). In order to geochemical classification of the samples, the K-Means clustering method classified the samples taken from the study area into four geochemical groups. The second group, which comprised a total of 422 composite samples, contained high-concentration CaCO3 samples with an average concentration greater than 97%. The average SiO2 concentration in these samples was 1%, and the sum of A l2O3 and Fe2O3 was about 0.38%. Therefore, the materials falling in this group can be appropriately used as raw materials to feed the plant. Using clr-transformed data for R- and Q-mode analysis, the eigenvectors and eigenvalues show sub-compositionally coherent and closure effect is overcomes. The total variance which is explained by the first factor decreases from 78.6% in real R-mode analysis to 61.7% in clr transformation. Due to major occurrence of the first dimension on geochemical changes, the samples categorized as richest (the best data set), contaminated (the worst data set), and transmission zone. Keywords Dariyan Formation · R-mode factor analysis · K-Means clustering · Precipitated calcium carbonate (PCC) · Micritic and crystallized limestone · Clr transformation
1 Introduction Although the limestone formation is widely extended across Iran, not many areas are available from which precipitated calcium carbonate (PCC) can be industrially produced. The PCC is the most important product produced
from limestone mineral processing [41]. This product is used in a variety of industries including pharmaceuticals, paper industries, and cosmetics [23]. Papers made from CaCO3 have a
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